2021/12/17 by Fang Chen, Chen, Fang, Aihua Zhang +6
Engineering · Environmental Science · #Advanced Chemical Sensor Technologies #FOS: Computer and information sciences #Machine Learning (cs.LG) #Oil Spill Detection and Mitigation #Water Quality Monitoring Technologies
paper · pdf · doi:10.48550/arxiv.2112.09638
openalex publication_date 2021/12/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Segmentation of marine oil spills in Synthetic Aperture Radar (SAR) images is a challenging task because of the complexity and irregularities in SAR images. In this work, we aim to develop an effective segmentation method which addresses marine oil spill identification in SAR images by investigating the distribution representation of SAR images. To seek effective oil spill segmentation, we revisit the SAR imaging mechanism in order to attain the probability distribution representation of oil spill SAR images, in which the characteristics of SAR images are properly modelled. We then exploit the distribution representation to formulate the segmentation energy functional, by which oil spill characteristics are incorporated to guide oil spill segmentation. Moreover, the oil spill segmentation model contains the oil spill contour regularisation term and the updated level set regularisation term which enhance the representational power of the segmentation energy functional. Benefiting from the synchronisation of SAR image representation and oil spill segmentation, our proposed method establishes an effective oil spill segmentation framework. Experimental evaluations demonstrate the effectiveness of our proposed segmentation framework for different types of marine oil spill SAR image segmentation.